When you model a product in Pickler, each material needs to be mapped to the best matching dataset in IDEMAT.

IDEMAT works with representative environmental datasets for common material categories and production systems, rather than every commercial material name on the market.

This can sometimes make mapping difficult. In reality, there are thousands of:

Most of these materials do not exist as a 1-on-1 match in IDEMAT.

Instead, the goal is to find the dataset that best represents:

In this guide, you will find best practices and practical tips to help you map materials more consistently and better understand the logic behind professional LCA modeling.

You did a search in Pickler and couldn't find a match, try the steps below:

Start by asking Pickler AI which IDEMAT material is the best match.

Pickler AI can:

This is usually the fastest way to find the correct material.

Example questions:

You can also upload:

to help the AI provide better mapping suggestions.

However, it is important to understand that Pickler AI provides guidance and suggestions based on available data and common LCA modeling practices. The final responsibility for selecting and validating the material mapping always remains with the user.

Pickler is not an independent LCA consultancy or verification body and does not validate whether a specific mapping choice is scientifically “correct” for your exact product or use case.

Read more:

If there is no direct match in IDEMAT, the next step is to identify what the material is actually made of and choose the dataset that best represents it.

Commercial or supplier names are often created for sales, branding, or ERP systems, but they usually do not describe the actual environmental material profile needed for LCA modeling.

For example:

If you are unsure, the best next step is often very simple: ask your supplier what type of material it actually is.

Your supplier can often directly tell you:

Useful documents include:

Once those characteristics are clear, it becomes much easier to find a representative IDEMAT dataset or proxy.

Once you understand what the material is actually made of, the next step is to find the IDEMAT dataset that best represents it.

In many cases, there will not be a perfect 1-on-1 match. This is normal in professional LCA modeling.

Instead, the goal is to choose the dataset that best represents:

For example:

A good proxy is:

If multiple datasets seem possible, choose the one that most closely reflects how the material is actually produced and used in practice.

Check whether the material should first be split into separate material lines.

For example:

Recommended structure:

Why?

Because recycled PET and virgin PET should be mapped to different IDEMAT datasets.

The same logic applies to:

Read more:

No LCA model is perfect.

The goal of Fast-Track LCA is not to create an infinitely detailed scientific model for every possible product variation. The goal is to:

A good model is:

A common mistake in LCA modeling is trying to model every supplier-specific detail, coating, additive, or commercial variation separately, even when these differences have little impact on the final result.

In practice, environmental impact is usually driven by:

That is exactly why representative secondary databases such as IDEMAT exist. The goal is not to perfectly recreate every supplier process, but to model products consistently using the best available representative datasets.

A common mistake in LCA modeling is spending too much time collecting highly detailed low-impact data, while overlooking the major environmental drivers of the product.

To keep models scalable and manageable, Pickler uses a 2% cutoff guideline.

Components below approximately 2% of the total product weight usually do not need separate modeling unless they significantly affect the impact.

This often applies to:

The goal is to focus modeling effort on the data that creates the most decision-making value.